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October, 2020

How IoT is Improving the Quality of Healthcare

How IOT Is Improving The Quality Of Healthcare

IoT is redefining Healthcare. Look around and you will find people with smart devices that track their every move, calculate their intake, and give them trends on this data.

Primitively caregivers and hospitals were using telemetry to remotely gather data for improving patient care. The primary aim of preventive healthcare was to deliver personalized care, improve patient care without spending a huge amount of money.

The Internet of Medical Things (IoMT) is driving the future of the healthcare industry. It can bring better outcomes; improve efficiency and make healthcare more affordable, as caretakers are increasingly resorting to more self-care due to increased awareness. To achieve this, healthcare providers must make use of the latest technology in a more systematic way.

The Scope of IoT is Getting Bigger and Better in:

  • Preventive healthcare: by use of wearables.
  • Patient tracking: in monitoring patient movement and health analysis.
  • Geriatric care: in tracking senior citizens which is a large market for IoT and medical devices.
  • Real-time location tracking: in tracking medical devices, people, and asset movement.

It is predicted that the revenue from smart wearables will increase to around $22.9 billion by the end of 2020. Experts from P&S Market research expect that the Internet of Things industry will grow at a compound annual growth rate (CAGR) of 37.6% between 2015 and 2020.

Wearables for Preventive Health Analysis 

Imagine a wearable is used for preventive health analysis. The term wearable in health parlance should not be restricted to just fitness tracking devices worn in the wrist that is used to monitor personal health.

The term wearables should go beyond the tracking of physical activities. It could be used as a communication device or it could even be a device that interacts with other devices like an Apple watch. It could be a device in the body, on the body or near the body like a medical app that helps track personal health;

Some of the leading medical apps that are already disrupting the healthcare market are:

  • Philips’ Medication Dispensing Service
  • Boiron Medicine Finder App
  • Future Path Medical’s Urosens

Digital Hospitals Making a Headway

The Healthcare industry is increasingly leveraging modern technology and digital hospitals are making headway such as the Humber River Hospital in Toronto Canada and the Medical Center at Mission Bay San Francisco. Innovative approaches towards engaging robots in the radiology and other departments are also disrupting the way healthcare is delivered.

Deakin University Australia, in partnership with Telstra Australia, has developed haptics-enabled robots that can perform ultrasound diagnostics remotely. This means the patient need not be in the same place as the sonographer conducting the ultrasound.

IoMT for Improved Healthcare

There are over 97,000 mobile healthcare apps as of 2019 and the mHealth app marketplace is expected to grow 15 times faster, according to a survey. Another survey indicates that users prefer digital services to communicate with doctors, monitor health, and collaborate with caregivers with ease.

Final Thoughts

IoT is clearly here to stay. With the cost of Hardware coming down, there’s no dearth in demand for wearables as this space is just short of an explosion, especially, in the mature markets.

IoT devices and apps are helping healthcare professionals in providing better care for their patients. There is definitely much scope for advancement for IoT in the healthcare sector.

Looking to leverage IoT technology for a healthcare solution? Kindly Contact Us here.

Improve Retail Business With Machine Learning

Improve-Retail-Business-with-Machine-Learning

Technology has transformed how customers and brands communicate with each other. Shoppers were once dependent on face-to-face, in-store interactions to make purchases and receive support. Now, shoppers do their research before entering a store (81 percent of shoppers conduct online research before buying) and hardly rely on salespersons to help them make decisions. Retailers, however, have understood that by embracing technology, they can extend their storefronts to their customers’ fingertips.

Shoppers can make purchases from within social media apps and compare prices without leaving a store. While these technologies have propelled the retail industry further into the digital age, the technology that is still evolving will have the largest impact on the future of the customer service and retail industries.

Embracing Big Data

More retailers are tracking customer shopping habits through data sources such as social media, purchase history, consumer demand, and market trends. By relying on big data technology to gain a deep understanding of shoppers and their buying trends, retailers can maximize customers’ spending and encourage customer loyalty.

According to research by Accenture report, 70 percent said that big data is necessary to maintain competitiveness, and 82 percent agreed that big data is changing how they interact with and relate to customers.

Matching Products with People

Machine learning technology boosts the reach of big data analytics and can help create an exceptional shopping experience. Innovative retailers can tap into the power of machine learning algorithms to do things like determine available products from outside vendors or recommend the quantity, price, shelf placement, and marketing channel that would reach the right customer in a particular area.

Further, the capability to automate everything through advanced analytics and machine learning soon will mean that basic customer service will be performed by bots that can predict our needs and provide service in the fastest, most immediate way possible: by offering us items we didn’t know we needed. As retailers gain more insight into their customers and products, machine learning will be able to match buyers and sellers based on buyers’ needs and product availability.

Digital Assistants

Shopping is becoming increasingly programmatic. In the future, services like digital assistants (Siri, Cortana, etc.,) will learn more about us and offer us relevant and personalized product offers. Say, for example, you use a particular brand of perfume. Your digital assistant will learn your shopping and usage habits and offer you the best deal on the product at the right time. It might even place the order for you.

Improving the backend

Machine learning and advanced analytics will not only change how we shop and provide customer service, but also simplify how retailers perform basic operations. Data science and machine learning give us the ability to automate so much of the heavy lifting required to find insight within a pile of data. With these tools, retailers can find useable and useful data to change the shopping experience for consumers.

Technology enables us to create an index of every product in the world, enabling retailers to offer customers the best prices, keep products adequately stocked, and track competitors’ minimum-advertised-price violations. A central database of the world’s product information enables retailers to offer the best shopping experience for buyers.

An innovative-technology approach to customer service and commerce will combine data about our behaviors and choices with data about products and product attributes to create the best shopping experience. This approach takes the guesswork out of purchasing and makes the shopping experience more cherishable for everyone.

Top 5 Big Data Trends In 2020

Big data

When the world big data rapidly expanded a decade ago, there were no signs that they would slow down. It is primarily aggregated across the internet, such as social networking, web search requests, text, and media files. IoT devices and sensors produce another gigantic share of data. These are the main reasons for the global big data market growth of 49 billion dollars.

Spark will Widespread

Apache Spark is a platform for data processing that can easily perform tasks on very large data sets and also spread the functions of data processing over many devices, either on its own or in combination with other distributed computing resources. These two qualities are important to the worlds of big data and machine learning that require vast data stores to sharpen the masses of computer power. Spark removes some of the programming burdens from developers with an easy-to-use API which sums up many of the grunt tasks of distributed computing and big data processing.

Apache Spark has been one of the main computing frameworks that spread throughout the world. Spark offers native binding for Java, Scala, Python, and R languages, and supports SQLs, data sharing, machine learning, and graphic processing. The Spark software can be used in several ways.

The convergence of IoT, Cloud, and Big Data

In order to facilitate interaction between machines and humans (M2H) and machines (M2 M), the Internet of Things is an opportunity for simplifying operations in many areas. Until now it has been greatly improved. In most cases, sensor-generated data is transmitted for analysis to the Big Data System and final reports are made. This is also the main interconnecting point of the two technologies.

For the next ten years, IoT is expecting a future of $19 trillion in the web industry, which will give room for more IoT and Big Data research and development.

Cloud computing plays a significant part in the storage and management of the data by generating an immense amount of data. It is not only about big data growth but also the development of platforms such as Hadoop for data analytics. As a consequence, it provides new cloud computing opportunities. Therefore, service providers like AWS, Google, and Microsoft have cost-effectively their own Big Data Solutions for businesses of all sizes.

Mixed Reality will improve Data Visualization

AR and VR have gained a lot of traction among customers in the past few years. With the launch of Pokémon Go, Augmented Reality had garnered around 100 million users within just a few weeks of launch. Though AR or VR might not be very useful for large corporations, the concept of Mixed Reality might very well be. Mixed reality combines the virtual world with our real-world and devices like Microsoft Hololens are already gaining traction. Mixed Reality will offer huge opportunities for organizations to better perform tasks and also to better understand the big data.

Deep Learning

Deep learning is an advanced form of machine learning which is based on neural networking. Deep learning help recognize specific items of interest from massive volumes of unstructured data. It is mostly useful for learning from huge volumes of structured and unstructured data. Thus businesses and organizations should pay more attention to deep learning algorithms to deal with the heavy influx of big data.

Data Virtualization

Data virtualization will see strong momentum this year. Data virtualization has the ability to unlock the hidden concepts and conclusions from a large set of data. It also allows enterprises and organizations to retrieve and manipulate data on the go.

To address big data problems, the management and use of computer and data-intensive systems require huge amounts of highly distributed datagrams. Virtualization offers the additional flexibility needed to realize large data platforms. Although virtualization is theoretically no prerequisite for big data analysis, in a virtualized environment software frameworks are more effective.

Conclusion 

As mentioned earlier, this year will be an exciting year for big data, and analytics systems will become the top priority for organizations. These systems are expected to perform well operationally, and fulfill promises of business value to the organization.

Virtual Digital Assistants: Is it the rise of machines already?

The term Rise of Machines should bring you memories of the movie Terminator series and Skynet.

Virtual digital assistants (VDAs) are rapidly gaining traction in both consumer and enterprise markets. So, what are these VDAs? Virtual digital assistants are nothing but automated software programs or platforms that help the user through understanding natural language in written or spoken form. Going by the current trend, virtual digital assistants are poised to digitally transform the user experience.

Apart from smartphone-based virtual digital assistants which are widely popular, VDAs are also beginning to enter other device types like smart home systems, fitness trackers, PCs, and automobiles. This rapid proliferation of virtual digital assistants is due to the accelerated innovation and scalability of associated technologies like AI and NLP (natural language processing).

In the future, you will be able to chat with your car about the best locations to visit. Your car will display the best possible route after analyzing driving time and getting your preferences conversationally. All these advancements will be due to the tremendous power of virtual digital assistants.

From the consumer-oriented virtual assistants like Siri, Amazon Echo, etc., to dedicated software for business use cases, virtual digital assistants are going to digitally transform the customer experience. Thus, an enterprise must build a VDA to stand out from the crowd. Here are some of the steps to build an effective virtual assistant.

Step 1: Build a flawless speech-recognition system. This process requires acoustic modeling, voice modeling, and a speech recognition engine

Step 2: Enable Natural Language Processing (NLP) which is the basic intelligence required to process semantics of a user’s speech input.

Step 3: Integrate machine learning or AI to improve the intelligence of the virtual digital assistant. This allows VDAs to learn, understand, and adapt based on the information available.

Step 4: Since responses should be instantaneous, VDAs need large scale systems that provide the power required for processing large amounts of data.

Step 5: Finally, all these modules should be secured using an API gateway to interface with several other systems. It is worth mentioning that, VDAs should be designed for a mobile-first and cloud-based environment.

HOW COULD VDAS IMPACT THE CUSTOMERS AND BUSINESS?

VDAs would soon lead to the era of high customer satisfaction. With VDAs, there is tremendous opportunity to better engage customers and employees alike.

Customer services will become more proactive. Virtual assistants will learn more about you from your texts, searches, emails and it will start suggesting or predicting what you need, even before you ask.

Nexus – Inspiring Innovation

Nexus

Culture and Employee Engagement  

Every business strives to get this right – it’s work culture. Workplaces are usually very dynamic and varied in its surroundings. Developing an inclusive work culture involves the strengths and weaknesses of employees.  

Employee engagement is also one of the most contentious activities companies routinely endureAlthough there is little controversy about the importance of employee participation, the quality of the process, and how companies use it to create a high-performance workforce are very relevant. 

Life at TVS Next 

TVS Next helps clients reimagine, design, and develop software to make this world a better place. We work with people and organizations that are driven by ambitious goals.  

Career Aspiration

But building a better world starts with becoming better individuals, teams, and communities. We therefore consistently strive to help every Nexian evolve and achieve an inspired career path. Help them unleash their unlimited human potential!  

Growth of Nexians  

The career path is one of the most important journeys in our life. Leading oneself through career zigzags is like walking through a hedge maze, and what helps navigate the uncertain terrain is when aspirations are not capped by a ceiling, but at the same time grounded 

Career Growth

To achieve one’s potential, expectations are vital and must be guided to be meaningful. We look forward to a career path that is meaningful and leads us to make a positive impact around us and the world at large. Not a job, but a path that supports our aspirations. However, a career is more than just a job, or work, or occupation. It also includes one’s progress through life, growth, and development in vocational and avocational areas of life.

From setting high aspirations to realizing them we require a growth path. A path that is holistic, performance-driven, and enriches the value delivered. To ensure success, a growth path cannot be tread alone but in teams and requires collaboration, leadership, and a resilient attitude.    

Career growth paths require to go beyond individual contributions and take a progressive approach.A career path gives the employee a sense of direction, a way to assess career progress, and an opportunity to achieve career goals and milestones along the way. 

Career Transformation
 

But to sustain a growth path, it is vital to look beyond team contribution and solve complex problems that can contribute to the greater good of the organization and society.

Nexus – Inspiring Innovation 

Nexus is a People Experience Platform built by Nexians to help us achieve career and culture transformation from being performance-driven to a leading innovative culture.

Nexus is built on three pillars that enable transformation and measure growth Performance, Success, and Innovation.

This is just the beginning! 

Enterprise mobility roll-out: What are the key enablers?

Developing and implementing a foolproof Enterprise Mobility plan involves a well-organized thought process that considers all the possibilities, technicalities, obstacles, and risk factors. So, what exactly do businesses need to know about a plan for business mobility? There are more than just a few factors!   

Mobility has changed the way businesses interact with their key stakeholders. While enterprise mobility is becoming a necessary organizational strategy, companies are finding it challenging to implement this across the board. Here are a few things to keep in mind while rolling out a mobility strategy in your organization.  

Always Keep the Big Picture in Mind   

Even before an enterprise mobility plan is put in motion, businesses should first ensure that they understand what business mobility is and why they need it. Around the same time, they need to assess how much experience they have in the area of emerging technology and what the organization will accomplish by implementing these innovations. It is important to determine the means of business mobility and understand what you want your end goal to be in terms of business outcomes. Switching to mobility is a change that involves a considerable paradigm shift, and it is important that the change takes place smoothly. It is also important to understand how your key stakeholders’ interface with your business and consume your business products and services, and then, understand whether mobility will give you opportunities best befitting the consumption.  

Identifying the Appropriate Approach  

The next step is to build a holistic plan that encompasses all the criteria and requirements of business mobility, including buy-in time and engagement from decision-makers as well. When the organization decides how business goals and priorities can be best guided by mobile applications that will help improve employee engagement and company sales, a program of strategies would have to be initiated. The mobility strategy for businesses includes clarifications on the company’s digital maturity level, company’s vision, employee training requirements, information and safety requirements, mobile technology implementation and incorporation, customer experience effect.  

There are three basic approaches to building a mobile app – mobile, native, or hybrid apps. Deciding upon which one works best will help in the transition better. For instance, if you’re taking your first step into the world of mobile, responsive websites are perhaps a better bet. While native mobile apps are custom made, they are a lot more dynamic and engaging. Hybrid Apps are a combination of native apps and mobile sites. Click here to see a short presentation on the different types of mobile apps and what perhaps will work best for your organization.

Ensuring Continuous Application Delivery  

It is important to understand that the mobile is a device that offers frequent updates that are consistent, for the end-user. Mobility will, therefore, require a good number of revisions, shorter development cycles, and support across a range of mobile platforms. An organization should have specialized personnel and technology infrastructure to develop the tools that can help make this transition better.   

Managing Data  

If your enterprise generates a large amount of data, building a mobile infrastructure around your existing data capabilities is critical. It is important to understand how your key stakeholders interact with their mobile devices and ensure that they have access to the data they require. Through mobile applications, data can be delivered anywhere & anytime thereby resulting in increased productivity and operational efficiency.  

Rely on an End-to-end Security Strategy  

As the workforce becomes mobile, it is more challenging for organizations to secure their data. Taking appropriate security measures is critical while implementing a mobile strategy. It is important to allow employees to access data when they need it, while not allowing them to store in their devices. While empowering workers to get the most out of their devices and applications, appropriate policies and security solutions should be enforced for users, applications, and devices respectively. Remote access for authorized users, encrypting information, data leakage protection, identity, and access management, policy management and compliance reporting are some of the key security measures enterprises should adopt.  

Customer Cognizance Through Omnichannel

Customer-cognizance-through-omnichannel

In the last decade, marketers have progressed slowly but steadily. It was not uncommon for marketers, 10 years ago, to focus primarily on their domain and use SEO and SEM for traffic management. Marketers cannot condone such a gap in their perception of the customer in today’s omnichannel environment. 

Omnichannel

Recently, marketers have also begun to use other channels such as mobile displays, social media, and programmatic displays. Initially, these channels have been managed independently to produce “multi-channel marketing,” but later, they integrated their channels to provide a “cross-channel” brand message. 

For companies that are focused on attracting and retaining consumers with more choice and higher expectations, omnichannel marketing has become a priority.  

Like other trades in the modern era, omnichannel is an area of tremendous potential, frequently debated, but seldom well accomplished. This practice includes addressing obstacles and uncertainties that hinder other businesses from making their true vision happen. 

There is no doubt that data is growing at an exceptional rate and, especially, the increasing number of consumer data fundamentally alters the way brands work in the region.  

Given the impending future without cookies, improvement in personalized targeting increased with consented customer information such as Consumer Data Platforms and Consumer Panels. The growth in 1st-party data provides marketers with an excellent view of the customers in their online world. Companies are also increasingly equipped to furnish marketers with offline customer data, such as Point-of-Sale (POS) systems, geofencing, and beacons. 

Impacts of Isolated Online and Offline Data 

The inability of a brand to integrate its data online & offline adversely affects the ability of a company to intelligently trigger the omnichannel, holistic strategic considerations, and exploit media usage nuances. Their value at a scale is all at the ultimate cost of the marketer’s desired return on media expenditure due to a lack of understanding and a lack of attribution between online & offline world. Silo-based media and two-dimensional digital media consumption are still high across the Asian Pacific both for online and selected offline outlets. 

Digital’s share of ad spend across the region will expand from just over 50% in 2019 to 59% by 2023, with online video contributing 20%, and although less spent on conventional offline media platforms in general, these traditional channels still account for a substantial proportion of promotional budgets. Out-of-Home, is still going strong in the South East Asia region, having grown +19% in 2019. In planning for these campaigns, brands are of course using the rich data for both their online and offline consumers to inform their segmentation and targeting. The issue is that both data sets live in isolation from one another, and media planning teams are unable to use online data to influence offline media planning and measurement, and vice versa. The lack of a bridge results in media planning that is divorced from the complex reality of how consumers behave across channels.  

Consumers aren’t two-dimensional, unlike our existing online and offline data usage. They do not live in online and offline siloes, and their purchases are no longer predictable.  

Most marketers are familiar with the all-round concepts of web-building (where consumers collect information about the goods online and then buy them offline) and showrooming (where consumers are visiting the stores physically to look and feel the product before making their purchase online), But even those concepts are oversimplified representations of how dynamic the purchase journey has become. Consumers do not flow constantly through a funnel but a maze of actions and connections with the brand in the path to purchase as they move through online and offline touchpoints. In fact, understanding this maze and getting a single view of the consumer when they seamlessly move online and offline is critical. 

Bridging the Gap Between Online and Offline Data 

Digital networks have been leading the way since they attempted to link or bridge the gap between offline and online platforms. Why is it required? Why cannot customers get a different experience for each channel? It is because, with the advent of digital platforms, consumer perceptions have changed drastically. 

omnichannel

Easier to communicate with brands using digital technologies led them to expect smoother and seamless offline/physical brand interactions. In short, they wanted an all-round experience that would blur the distance between offline networks.

Cross-channel marketing is all about delivering marketing strategies to consumers through different platforms both offline and online. 

The marketers must merge networks with each other in order to bridge the gap between the offline-online, to transfer data, and to process it from one channel to the other. Different technologies like APIs, RFID, etc. may be used. 

Vanity URLs often act as a cross-channel marketing tool that reduces offline distance. Such URLs are shortened web addresses that are easy for customers to recall. Using these URLs on offline assets like a flyer, print ad, or banner, consumers can be motivated to search the URLs on phones, tablets, or computers. 

Marketers will sell the goods online and have their customers picked up from the brick and mortar shop to create a seamless shopping experience.  

And salespeople can also allow products bought online to return to a physical shop. Items bought from physical stores can also be returned digitally by requesting a pick-up of the items. The cashback can also be credited directly to the customer’s bank account. 

There was one of America’s famous retailers who experimented in bridging the gap between online and offline data—Macy’s. The company noticed that consumers frequently review items on their website before visiting a physical store. The company thus wanted to give consumers exposure in the store in order to see if their favorite items were available in the nearest shop. Macy also offered various shipping options, such as home delivery, click-and-collect, etc., which really resonated with consumers and helped increase the company’s revenues. 

Marketers may use location-based targeting to drive consumers to purchase products while they are on the go. 

It can be achieved by providing a smartphone push notification when they enter or exit a geofenced area. They can also be monitored by beacons when entering the shop. Marketers use beacon technology to give customers a personalized experience while they shop. Therefore, the offline-online link is enhanced and at each stage of the customer journey, there is an interaction. 

For instance, a person enters the mall and triggers the geofence. An app pushes notification pops-up on his device about some exciting offer going on in his favorite store. The customers are encouraged to visit the store even though they do not intend to go to it in the first instance in real-time. 

Conclusion 

When consumers expect enriching experiences across all touchpoints, irrespective of online and offline platforms, marketers will strive to achieve that. A clear transition should be made from an offline to an online platform from the consumer viewpoint, and vice versa. To step up the ladder, marketers must follow omnichannel marketing strategies that primarily bridge the gap from offline online to provide consumers with a clear and seamless experience across all available channels. 

Will Automation Eliminate Manual Testing?

We live in an era where software development has been revolutionized by AI (Artificial Intelligence) & ML (Machine Learning). It is expected that testing will be taken over by automation with its new developments and advancements, but that is not the case. Software manual testing has been around for many decades since initial software development, and the industry has taken multiple shifts. However, its scope remains the same. In this article let us explore the impact automation testing has over manual testing.

Why is manual testing still relevant?

New Projects: Projects in pilot phases which begin as a concept and take shape during early sprints require testing to be done manually. Using automation testing during the initial phases of a project would be expensive as it undergoes continuous changes. Using manual testing in these cases would be cost-efficient and easy to accommodate changes.

End 2 End Testing: Automation testing can be used to test single systems or integration levels in detail. Whereas, End 2 End testing involves multiple systems and require manual testing. Automation testing that runs an End 2 End test scenario has many challenges, especially systems have different tech stacks. Design changes involving systems in End 2 End testing impacts maintenance cost.

Maintenance Cost: For small projects or components, automation testing costs higher than manual testing. Performing quick manual testing would suffice for smaller projects/ components that undergo frequent changes rather than updating test scripts after rerunning those tests manually.

UX Testing: Maintenance costs are proportional to UX changes. Each time UI/UX change, test cases break and raise a false fail. When changes are encountered in a script, there are rework/ maintenance to achieve the test pass. This impacts the next UI changes again. So, for an application with frequent UI/UX changes, automation testing is costlier than manual testing.

Visual Testing: While there are few automation tools available in the market, AI & ML are incorporated with visual testing to achieve 100% test result. But the number of hours required to train AI to understand minute changes in UI would be expensive than performing manual tests. Sometimes, human eyes can find a little misaligned text boxes, which could be challenging for an automation tool. Such automation tools with AI & ML are expensive compared to manual testing.

User Acceptance Testing: There is no way user acceptance testing could be automated. Beta users/client team must experience the end product by simulating user experience using manual testing.

How automation testing can be leveraged?

Let us discuss the areas where automation has to be implemented to support manual testing efforts.

Regression: When a part of the product is regression and the product or UI changes, tests have to be automated using open source software. Using automation testing can therefore save manual testing time.

Integration Testing: API level automation can be quickly created like manual testing. Tools like Postman enables to create tests that can be automated using runner feature. When manual testing is performed, requests are stored as collection. This stored collection can be run any time as a test suite to rerun the test scenarios.

Smoke Test on CI/CD: Automating test scripts for smaller projects are expensive. However, using smoke test scenario would reduce the cost. Smoke tests undergo changes to get added to CI/CD pipeline for project code deployment capturing blocker/showstopper issues during code deploy to QA/Stg environment, before the code is released to production.

Conclusion:

Manual and automation testing complement each other. Manual testing is as important as automation testing and there can be no project that is purely manual. There will always be an area where automation can be leveraged with open source tools which are no-cost and low maintenance. No project can completely use automation testing as client expectation keeps changing; manual testing is the way to handle frequent changes and ad-hoc testing requests. It is up to the project management to decide how and where manual and automation testing have to be implemented to provide a customer satisfied product delivery.

Emerging Healthcare Trends Post Covid-19

COVID-19 reveals how fragile many world’s health systems and services are, forcing countries to make difficult choices to best meet people’s needs. Though patient care systems have progressed long before coronavirus disruption, online health consultations, involvement of healthcare providers, and remote monitoring of patients are becoming increasingly accepted.

New approaches to delivering healthcare services

Improvement in healthcare can be achieved irrespective of geographies. New healthcare approaches are paving way with modern infrastructure, service requirements and analytics.

1. Enhanced pharmacy experience: Pharmacies are expanding their touchpoints to provide medicines and other healthcare services. Grocery stores are also being used to sell drugs along with other products to retain customers and give them single time purchase experiences. Many leading brands are exploring options to set up healthcare retailing and experimenting delivery of medicines using drones.

2. Increase in contactless experience: Coronavirus apprehension is more prevalent in clinics and hospitals. Technology-enabled workflows are used to improve patients’ registration before visiting a clinic. Facial recognition programs are used to remove the need of touch, requiring registration stands in hospital lobbies. Routine tests are also getting virtual, and many diagnostic procedures via remote-controlled devices are increasing. With the rise of bandwidth, modern mobile apps, and desire to isolate themselves from society throughout the pandemic, both clinicians and consumers are preferring virtual calls. Virtual video care is been initiated in many locations around the world to reduce long-distance travel for patients.

3. Big Data in healthcare: Big data uses the collection of all data points on COVID-19 from around the world. These data are used by mathematical models to identify geographic locations, establish mortality prediction models, estimate testing and test delivery requirements, and guide policymakers, healthcare providers, and other key players in decision making. It is essential to note that while big data gives us the perspective that we wouldn’t have otherwise, all variables required to make a specific decision are not automatically considered.

4. AI and data analytics: Health industry’s defensive line against COVID-19 was primarily helped by AI and data analytics. Machine Learning and data analysis have a significant role in understanding the spread of the disease and the efficacy of the different responses to the infection. Studies have utilized these methods for monitoring the capacity of hospitals to classify high-risk patients, and many agree that AI could be used to plan for similar situations in the future.

Conclusion

A seamless product design is to gain an intuitive understanding of the trajectory of patients in the modern post-pandemic period and to identify high-impact touchpoints for digital interaction. Every patient population varies in their digital communication preferences, whether by socioeconomic status or other demographic factors. Each health system must develop a digital environment suitable for its patients, while continuing to address the needs of caregivers who provide and maintain the environment

Why It’s Vital for Companies to Focus on Data Engineering?

Digitalization is multiplying, making data the most prized asset in the world. Organizations are strategically moving towards insight-driven models where business decisions, process enhancement, and technology investments are handled with the knowledge gained from data. Big budgets are planned to make use of abundant data available, and this spending will only increase over the years. 

According to a recent IDC report, it is estimated that by 2025 the Global Datasphere will grow to 175 zettabytes (175 trillion gigabytes). It also states that 60% of this data will be created and managed by businesses, driven by Artificial Intelligence (AI), Internet of Things (IoT), and Machine Learning (ML). AI and ML are gaining mainstream focus among many industries, and global spending is expected to grow to $57.6B by 2021.

How Data Engineering is helping businesses succeed?

Organizations often consider Data Science to be the only method to gain meaningful insights necessary to drive their business goals. However, the real potential lies within Data Engineering, which allows companies to build large maintainable data reservoirs. These design data processes are scalable and ensure relevant data is available for Data Science and Data Analytics to process complex statistical programs and algorithms to provide useful results. Only with reliable and accurate insights created from diverse sources can help data analytics harness the full power of data. 

Today, AI and ML have become integral parts of organizations, helping them achieve higher operational efficiency, become agile, taper new market opportunities, launch new products with faster go-to-market, and provide higher customer satisfaction. But according to a survey done by MIT Tech Review, 48% of companies said that getting access to high quality and accurate data was the biggest obstacle in successfully implementing an AI program. To overcome this hurdle, businesses must focus on effective Data Engineering, which forms the basic building blocks for AI and ML.

Three advantages of effective Data Engineering:

1) Accelerates Data Science 

2) Removes bottlenecks from Data Infrastructure 

3) Democratizes data for Data Scientists and Data Analytics 

Once organizations understand and internalize this, it is easy to see how the potential of Data Engineering is limitless. 

Data-Engineering

How data engineering is helping businesses across industries

Industry influencers and other prominent stakeholders certainly agree that Data Engineering has become a big game-changer in most, if not all, types of modern industries over the last few years. As Data Engineering continues to permeate our day-to-day lives, there has been a significant shift from the hype surrounding it to finding real value in its use. 

Manufacturing

Industry 4.0 is here, and the sooner organizations start their digital transformation, the better equipped they become to handle the evolving market conditions. What Industry 4.0 has brought is a significant shift in how manufacturing businesses are changing from being purely process-driven, to becoming data-driven. This essentially means that companies are either adding new digital components or updating their existing components with digital features. However, this creates a complex technology landscape where legacy systems have to interact with modern systems. 

An effective Data Engineering solution can communicate and retrieve data from different systems, sort out critical data from a pool of data, and process them to be analyzed further. Data Engineering bridges the gap between Production, Research Development, Maintenance, and Data Science. Data Engineering can help in enhancing the critical aspects of manufacturing industry—production optimization, quality assurance, preventive maintenance, effective utilization of resources, and, ultimately, cost reduction. 

Entertainment

Data has the power to make or break a business, and no one understands this better than Netflix. The incredibly successful data-driven company uses insights across its business functions to decide what new content to invest in and launch, enhance operational efficiency, and, most importantly, provide predictive recommendations for its global audience. 

Netflix has also used its robust Data Engineering system to convert over 700 billion raw events into business insights, which is one primary reason why the company continues to be the market leader.

Retail

The retail industry is continuously trying to tap into new business opportunities by gaining insights from data sources across the physical and virtual ecosystems. To gain these business insights, data must be gathered from a large network (comprising of POS systems, e-commerce platforms, social media, mobile apps, supply chain systems, vendor management systems, inventory management systems, in-store sensors, cameras, and a growing list of new sources). 

An effective Data Engineering solution can bring together massive sets of structured and unstructured data from entire value chain to provide trends, patterns, customer insights, and more. A retailer with stores across the globe and an omnichannel presence can harness data sources in innovative ways with Data Engineering to gain a detailed understanding of the market, the competition, and every step of the customer journey. 

Healthcare

Leading healthcare giants are progressively investing in integrating ML into their core functions. However, they are focusing on setting up their data infrastructure by building Data Engineering platforms. The healthcare industry is looking to unlock value from data to gain knowledge into the patient, healthcare worker, and the healthcare system on a large scale. 

Data Engineering brings together insights from electronic patient records and hospital data, as well as new advanced data sources like gene sequencing, sensors, and wearables. It offers them to Data Analytics to provide better medical treatment. 

How Data Engineering is fueling the businesses of the future

To manage data at large scale and segregate business-critical data from the rest, organizations need a long-term data strategy plan to be future-ready with Data Engineering as critical approach. 

Data Engineering creates scalable data pipelines

Distributed data processing systems can help create reliable data pipelines with low level of network management to meet huge volumes and tap into increasing data sources in a growing ecosystem of touchpoints. 

Data Engineering ensures that data is consistent, reliable, and reproducible

For data processing to be successful through the stages of ingestion, analytics, and insights, it is important that the data be compatible by ensuring it complies with the required formats and specifications. Data science can derive better insights from data by providing reliable and reproducible data.

Data Engineering helps ensure that processing latency is low

Most essential business insights are required to be in real-time to have an effective impact, be it with customer experience in the retail industry or predictive analysis in the financial sector. If the data being analyzed has a significant time delay, the insights can be less effective or completely ineffective. 

Data Engineering optimizes infrastructure usage and computing resources

Using the right algorithm for data engineering can save a considerable amount of money spent on resources. This can provide significant savings to organizations and help them optimally utilize their technology landscape. 

Businesses must design Data Engineering solutions that are unique to their needs and create customized frameworks rather than follow trends. At the same time, many new start-ups begin their data journeys with clearly defined data sets. In contrast, traditional organizations may have larger ones from legacy systems and data sets from new sources. It is important to understand that while the Data Engineering tools for a particular organization are zeroed, no general rule can be used. Only a comprehensive study of a company’s unique technology ecosystem and business needs can determine the type of Data Engineering systems that should be used. 

Data Engineering solutions must also be flexible. How data is produced and consumed is constantly evolving, so Data Engineering solutions or frameworks must be flexible to accommodate future requirements. Guiding the movement in this direction is the shift from traditional Extract Transform and Load (ETL) methods of the data pipeline to more pliable patterns like ingesting, model, enhance, transform, and deliver. The latter provides more flexibility by decoupling Data Pipeline services. 

Many experts focus on Data Engineering one step further by encouraging companies to adopt a Data Engineering Culture. This permanently recognizes the need for Data Engineering at all levels of an organization across functions and warns that business predictions will fail without effective Data Engineering and an appropriate ratio of Data Engineers to Data Scientists. 

The sooner organizations push for Data Engineering Culture and create organizational alignment, the more equipped they will be for the future, to which data holds the key. 

How TVS Next created a Data Engineering solution for one of India’s top utility companies

In the energy sector, large enterprises are turning real-time data to drive effective energy management. Energy corporations rely on data for efficient resource management, operational optimization, reduced costs, and increased customer satisfaction with better insights into supply and demand in real-time. 

TVS Next helped one of India’s leading utility companies build a distributed computing engine for processing and querying data at scale. The solution provided the company with tools to visualize key performance indicators using real-time data. With effective Data Engineering, the client improved the customer experience rather than relying on complex algorithms to predict outcomes. 

What are some of the achievements and challenges you have faced while planning a Data Engineering system for your organization? Share your story and get in touch with us here